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The characteristics of synthetic aperture radar (SAR) image texture may be related to the properties of underlying elemental scene scatterers through established models based on the properties of a backscattering coefficient (in this paper, we use and unnormalized coefficient s ) of these scatterers. In this paper, we generate raw SAR data by simulating the statistical characteristics of elemental scene scatterers such as the order of the gamma distribution, the form and length of their spatial correlation, and their spatial density. This simulation is carried out using a SAR signal simulation system called cSAR. We describe a particular set of methods to simulate image texture used in cSAR, and provide a detailed analysis of simulated s and of the speckle and texture characteristics of simulated images. We found that the distribution of s was strongly affected by the order, density, and correlation length of the underlying scatterers. We found that the simulated SAR images were consistently K -distributed as expected. The estimated image order was a strong function of the scattering properties and that the estimated image order is a relatively weak descriptor of image texture when used on its own. The correspondence between the observed image autocorrelation function (ACF) and the theoretical models of Oliver is excellent, and we could estimate the scatterer correlation length by fitting the Oliver model to the observed ACF. We combined the estimated image order and correlation length and found potential for using these two image texture descriptors in classification and segmentation algorithms.